Full Steam Ahead: The 2024 MAD Machine Learning, AI & Data Landscape

Generative AI In Creative Industries Market Size 2032

Both of these generative applications (as well as Ernie) were built using PaddlePaddle, a Baidu-developed, open-source, distributed deep learning framework said to be the largest of its kind in China. These include the lightweight model for training smaller applications, the cross-modal model, which is a diffusion model for text-to-image, and a protein-prediction model specifically designed for predicting protein structures. Ernie is Baidu’s answer to ChatGPT and is the lynchpin of its plans to augment many of its products and services with generative AI. The friendly-sounding name represents Enhanced Representation through Knowledge Interaction. Here’s an overview and a look at what we can expect from one of China’s leading technology companies in the near future.

Patent Landscape Report – Generative Artificial Intelligence (GenAI) – 5 Patent trends in GenAI applications – WIPO

Patent Landscape Report – Generative Artificial Intelligence (GenAI) – 5 Patent trends in GenAI applications.

Posted: Wed, 03 Jul 2024 10:29:38 GMT [source]

Right now, CISOs at large organizations are primarily focused on discussions with AI security experts rather than rushing into product purchases. Their main goals are to understand how generative AI is being used, identify the key use cases that will find their way into production, and determine how their security team can support those use cases. First, the impact of generative AI on the threat landscape is significant because it’s easier than ever to forge people’s voices, images, writing style, and more. What used to be a manual process for attackers has become highly automated and scalable with the advent of generative AI, resulting in a surge of attacks like imposter scams.

The evolution of the SDLC landscape

Generative AI aims to produce new, original content or data that mimics real-world entities, trained on large datasets to understand and replicate complex patterns. Predictive AI, conversely, seeks to forecast future events or trends based on historical and current data, focusing on identifying patterns that inform its predictions. The handling of data also differs; generative AI requires substantial, diverse datasets to learn and create, while predictive AI necessitates high-quality, relevant data to accurately forecast future outcomes.

Semiconductors enable the underlying hardware for computation, facilitating the processing and complex calculations required for generative AI models. Generative AI is a subset of artificial intelligence that employs algorithms to create new content, such as text, images, videos, audio, software code, design, or other forms of content. The global generative AI in creative industries market size was valued at $1.7 billion in 2022, and is projected to reach $21.6 billion by 2032, growing at a CAGR of 29.6% from 2023 to 2032. As we approach the frontier paradox and as the novelty of transformers and diffusion models dies down, the nature of the generative AI market is evolving. Users crave AI that makes their jobs easier and their work products better, which is why they have flocked to applications in record-setting droves (in spite of a lack of natural distribution).

• Despite these limitations, the earliest Generative AI applications begin to enter the fray.
• Its seminal moment, however, came barely five years ago, with the publication of the transformer (the “T” in GPT) architecture in 2017, by Google.
• As this use case matures, expect to see more multilingual solutions with larger context windows, so longer and more complex queries can be posed.

Take, for example, SWE-bench, the canonical benchmark for tasks that the average human software engineer performs. We’ve gone from 1% to 4% to 14% to 19% on this key benchmark in the past 11 weeks. Geospatial analytics can grasp property size, construction, and condition by extracting structured data from high-resolution pictures using the power of Gen AI.

Nvidia: Powering the Generative AI Supercycle with Top-of-the-Line GPUs and Software Stack

In most regions and industries, AI usage remains largely unregulated, which can lead to a range of issues. Some users have already suffered consequences from their personal data becoming part of a model’s training data and potential outputs, while others have raised alarms about data storage and related security protocols in these solutions. While some AI vendors have independently chosen to make their training processes, data collection methods, and overall strategy more transparent, there’s little in the way of governing bodies to enforce this transparency.

As they build more functionality around things like workflow and collaboration on top of the core AI engine, they will be no more, but also no less, defensible than your average SaaS company. At the top of the market, the larger players have already been in full product expansion mode. It’s been the cloud hyperscaler’s strategy all along to keep adding products to their platform. Now Snowflake and Databricks, the rivals in a titanic shock to become the default platform for all things data and AI (see the 2021 MAD landscape), are doing the same.

Emerging Trends in the Generative AI Landscape

The proliferation of models and applications within the stack amplifies the volume of outputs requiring protection, thus increasing the risk of vulnerabilities. Generative AI may produce synthetic data that closely mimics the original dataset. The creation of synthetic data will aid in the training of machine learning models without revealing sensitive information. Preparing in this way will help generative AI to grow in a way that does more good than harm.

Apple, with its track record in creating trendsetting devices, is highly likely to introduce wearables that integrate seamlessly with its ecosystem within or alongside their XR Apple Vision Pro, offering unparalleled user experiences. NVIDIA, for instance, a leader in chip manufacturing which has been a clear winner in the AI race. The question on everyone’s mind is whether NVIDIA will expand its horizons into the cloud computing realm, leveraging its hardware expertise to offer integrated AI cloud services. NVIDIA already has a cloud streaming service called Geforce Now offering high end graphics processing on-demand. Such a move could redefine the competitive landscape, offering NVIDIA a more direct influence over AI’s developmental trajectory. Vector databases are a key requirement for more complex use cases of generative AI such as conversational memory, searching your documents (RAG), and also multi-modal solutions such as indexing images.

Additionally, there is a growing awareness of the potential for biases in AI algorithms, which can perpetuate discrimination. Addressing these issues requires a concerted effort towards developing AI with fairness, accountability, and transparency in mind, alongside robust data protection measures. The integration of AI technologies, both generative and predictive, into society raises significant ethical and societal questions. As these technologies advance, they challenge existing norms around privacy, security, and employment. The potential for AI to automate jobs has sparked debates on the future workforce and the necessity for a shift in skill sets. Moreover, the ability of AI to process and analyze vast amounts of personal data poses risks to privacy and data protection, necessitating a careful balance between technological advancement and the protection of individual rights.

This can be attributed to the remarkable capabilities of GANs in generating highly realistic and diverse content. GANs operate on a competitive framework, where a generator network creates synthetic data, and a discriminator network evaluates its authenticity. Through continuous iterations and improvements, GANs have demonstrated unparalleled success in tasks such as image and video synthesis, natural language generation, and creative content creation. Their ability to produce high-quality outputs with a wide range of applications across industries has made GANs the technology of choice for many companies, leading to their significant market share. The application layer in generative AI streamlines human interaction with artificial intelligence by allowing the dynamic creation of content. This is achieved through specialized algorithms that offer tailored and automated business-to-business (B2B) and business-to-consumer (B2C) applications and services, without users needing to directly access the underlying foundation models.

OpenAI doubled down with DALL-E, an AI system that can create realistic images and art from a description in natural language. The particularly impressive second version, DALL-E 2, was broadly released to the public at the end of September 2022. Reverse ETL companies presumably learned that just being a pipeline on top of a data warehouse wasn’t commanding enough wallet share from customers and that they needed to go further in providing value around customer data. Many Reverse ETL vendors now position themselves as CDP from a marketing standpoint. As a twist on the above, there’s a parallel discussion in data circles as to whether ETL should even be part of data infrastructure going forward. ETL, even with modern tools, is a painful, expensive and time-consuming part of data engineering.

Generative AI is poised to redefine software creation and digital transformation. As organizations plan their data analytics strategy, they must consider the potential use case of leveraging generative AI. Depending on the analytics maturity and business priorities, organizations can decide on a road map of whether and where they would leverage generative AI. With multiple issues such as accuracy and explainability, at least initially, organizations would be better off leveraging generative AI for specific use cases within the analytics value chain. Many supply chain leaders are wrestling with how to be at the forefront of the generative AI revolution. Combining advantages of both will maximize AI’s potential, improve adoption, and build competitive advantages for supply chains.

This strategy not only increases accuracy and effectiveness but also reduces cost overheads. Smaller models are not only cheaper to run but also quicker to adapt and easier to manage. Organisations now have a number of options of using readily trained generalised large language models such as OpenAI GPT, Google Bard, Anthropic Claude [RL model in chart below] or venture into the world of building your own.

In our view, these companies help illustrate the broader array of investment opportunities that are available across the entire AI value chain. Bursting more publicly onto the tech scene in late 2022 with the arrival of ChatGPT, within months, generative AI quickly began to radically reshape the tech sector. In fact, it’s no exaggeration to say that the “generative AI landscape” and the “overall tech landscape” are essentially merging into a single entity, as generative AI technologies find their way into a growing list of tech tools and solutions.

Entire categories became overcrowded within a year or two – data catalogs, data observability, ETL, reverse ETL, to name a few. The second wave is the ML/AI cycle, which started in earnest with Generative AI. As we are in the early innings of this cycle, and most companies are very young, we have been liberal in including young startups (a good number of which are seed stage still) in the landscape. The views expressed here are those of the individual AH Capital Management, L.L.C. (“a16z”) personnel quoted and are not the views of a16z or its affiliates.

Many of the first limitations slow down apps, while others might create real problems, like AI hallucinations, where generative AI apps make up content that’s not tied to facts. Some observers call generative AI a new general-purpose technology that could deliver the same kind of broad impact as the steam engine and electricity. “Basically, it frees up my cognitive bandwidth to focus on higher-impact and higher-value tasks.” In addition, generative AI has many applications, such as music, art, gaming and healthcare, that make it more attractive to the broader population. Retail and e-commerce are witnessing a revolution, courtesy of predictive AI’s ability to tailor shopping experiences.

• This difference underscores generative AI’s role in driving innovation through content creation, while predictive AI’s strength lies in its ability to analyze patterns and make predictions, supporting decision-making across sectors.
• Take GitHub Copilot, for example—this tool works directly with users’ GitHub accounts and ecosystems, assisting them with code completion, code snippets, troubleshooting, and plain-language code generation and explanations.
• This initiative not only supports governmental operations but also sets a precedent for other sectors to follow in harnessing the potential of generative AI responsibly.
• The dream is that generative AI brings the marginal cost of creation and knowledge work down towards zero, generating vast labor productivity and economic value—and commensurate market cap.
• The foundation layer of the Generative AI market is stabilizing in an equilibrium with a key set of scaled players and alliances, including Microsoft/OpenAI, AWS/Anthropic, Meta and Google/DeepMind.

However, rather than rushing to implement AI, I suggest business leaders set realistic expectations and goals for AI and understand the limitations of the technology. The fair use principle permits the employment of copyrighted material without authorization from the owner for purposes that are transformational. In essence, it balances protecting creators’ rights while encouraging creativity and innovation. But with generative AI creating original content from training data that may contain copyrighted works, things get nebulous. By keeping the human at the heart of generative product design, forward-thinking companies can form authentic connections with users and deliver truly differentiated value. This human-AI symbiosis is the hallmark of transformative product experiences yet to come.